Add Groq as Provider #609
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Add Groq as Provider #609
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WalkthroughThis update introduces support for the Groq LLM provider. It activates and updates the Groq provider configuration, implements a new Changes
Sequence Diagram(s)sequenceDiagram
participant User
participant UI (ModelIcon)
participant LLMProviderPresenter
participant GroqProvider
participant Groq API
User->>UI (ModelIcon): Selects Groq model
UI (ModelIcon)->>LLMProviderPresenter: Request for Groq provider instance
LLMProviderPresenter->>GroqProvider: Instantiate GroqProvider
GroqProvider->>Groq API: Fetch models / completions
Groq API-->>GroqProvider: Return model data / completion
GroqProvider-->>LLMProviderPresenter: Return processed model data / completion
LLMProviderPresenter-->>UI (ModelIcon): Provide data for rendering
UI (ModelIcon)-->>User: Display Groq model and icon
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Actionable comments posted: 0
🧹 Nitpick comments (1)
src/main/presenter/llmProviderPresenter/providers/groqProvider.ts (1)
26-28: Remove the unnecessary constructor.The constructor only calls super without any additional logic. TypeScript will automatically use the parent constructor when this is removed.
- constructor(provider: LLM_PROVIDER, configPresenter: ConfigPresenter) { - super(provider, configPresenter) - }
📜 Review details
Configuration used: CodeRabbit UI
Review profile: CHILL
Plan: Pro
⛔ Files ignored due to path filters (1)
src/renderer/src/assets/llm-icons/groq.svgis excluded by!**/*.svg
📒 Files selected for processing (4)
src/main/presenter/configPresenter/providers.ts(1 hunks)src/main/presenter/llmProviderPresenter/index.ts(2 hunks)src/main/presenter/llmProviderPresenter/providers/groqProvider.ts(1 hunks)src/renderer/src/components/icons/ModelIcon.vue(3 hunks)
🧰 Additional context used
📓 Path-based instructions (9)
src/renderer/src/**/*
Instructions used from:
Sources:
📄 CodeRabbit Inference Engine
- .cursor/rules/i18n.mdc
src/renderer/**
Instructions used from:
Sources:
📄 CodeRabbit Inference Engine
- .cursor/rules/project-structure.mdc
**/*.{js,jsx,ts,tsx}
Instructions used from:
Sources:
📄 CodeRabbit Inference Engine
- .cursor/rules/development-setup.mdc
src/main/presenter/**/*.ts
Instructions used from:
Sources:
📄 CodeRabbit Inference Engine
- .cursor/rules/electron-best-practices.mdc
{src/main/presenter/**/*.ts,src/renderer/stores/**/*.ts}
Instructions used from:
Sources:
📄 CodeRabbit Inference Engine
- .cursor/rules/electron-best-practices.mdc
**/*.{ts,tsx}
Instructions used from:
Sources:
📄 CodeRabbit Inference Engine
- .cursor/rules/error-logging.mdc
src/main/**
Instructions used from:
Sources:
📄 CodeRabbit Inference Engine
- .cursor/rules/project-structure.mdc
src/main/presenter/llmProviderPresenter/index.ts
Instructions used from:
Sources:
📄 CodeRabbit Inference Engine
- .cursor/rules/llm-agent-loop.mdc
src/main/presenter/llmProviderPresenter/providers/*.ts
Instructions used from:
Sources:
📄 CodeRabbit Inference Engine
- .cursor/rules/llm-agent-loop.mdc
🧠 Learnings (4)
src/renderer/src/components/icons/ModelIcon.vue (2)
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/vue-shadcn.mdc:0-0
Timestamp: 2025-06-23T13:06:15.336Z
Learning: Use Iconify/Vue for icon implementation, preferring the 'lucide:' icon family, and follow the '{collection}:{icon-name}' naming pattern.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/vue-shadcn.mdc:0-0
Timestamp: 2025-06-23T13:06:15.336Z
Learning: Import the Icon component from '@iconify/vue' and use it for rendering icons in Vue components.
src/main/presenter/configPresenter/providers.ts (6)
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Provider files should include provider-specific helper functions such as `formatMessages`, `convertToProviderTools`, `parseFunctionCalls`, and `prepareFunctionCallPrompt` as needed.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Provider implementations must use a standardized interface in their `coreStream` method to `yield` events, decoupling the main Agent loop from provider-specific details.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Each file in `src/main/presenter/llmProviderPresenter/providers/*.ts` should handle interaction with a specific LLM API, including request/response formatting, tool definition conversion, management of native/non-native tool call mechanisms (prompt wrapping), and standardizing output streams to a common event format.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : The `coreStream` method in each Provider must perform a single-pass streaming API request per conversation round and must not contain multi-round tool call loop logic.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Provider files must implement the `coreStream(messages, modelId, temperature, maxTokens)` method, which receives formatted messages and generation parameters, handles tool support (native or via prompt wrapping), makes a single streaming API call, parses provider-specific data, and yields standardized stream events.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Maintain separation of concerns by centralizing Agent loop logic in `index.ts` and keeping provider files focused on API interaction and event standardization.
src/main/presenter/llmProviderPresenter/index.ts (12)
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Each file in `src/main/presenter/llmProviderPresenter/providers/*.ts` should handle interaction with a specific LLM API, including request/response formatting, tool definition conversion, management of native/non-native tool call mechanisms (prompt wrapping), and standardizing output streams to a common event format.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Provider files should include provider-specific helper functions such as `formatMessages`, `convertToProviderTools`, `parseFunctionCalls`, and `prepareFunctionCallPrompt` as needed.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Provider implementations must use a standardized interface in their `coreStream` method to `yield` events, decoupling the main Agent loop from provider-specific details.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/index.ts : `src/main/presenter/llmProviderPresenter/index.ts` should manage the overall Agent loop, conversation history, tool execution via `McpPresenter`, and communication with the frontend via `eventBus`.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/index.ts : The main Agent loop, including the `while` loop for conversation flow, state management, provider interaction, event handling, and frontend communication, must be implemented in `src/main/presenter/llmProviderPresenter/index.ts`.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Provider files must implement the `coreStream(messages, modelId, temperature, maxTokens)` method, which receives formatted messages and generation parameters, handles tool support (native or via prompt wrapping), makes a single streaming API call, parses provider-specific data, and yields standardized stream events.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : The `coreStream` method in each Provider must perform a single-pass streaming API request per conversation round and must not contain multi-round tool call loop logic.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/streamEvents.ts : Define the standardized stream event interface (`LLMCoreStreamEvent`) in a shared location, such as `src/main/presenter/llmProviderPresenter/streamEvents.ts`.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Maintain separation of concerns by centralizing Agent loop logic in `index.ts` and keeping provider files focused on API interaction and event standardization.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/electron-best-practices.mdc:0-0
Timestamp: 2025-06-30T12:23:13.338Z
Learning: Applies to {src/main/presenter/**/*.ts,src/renderer/stores/**/*.ts} : Implement proper error handling and logging for debugging
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/electron-best-practices.mdc:0-0
Timestamp: 2025-06-30T12:23:13.338Z
Learning: Applies to {src/main/presenter/**/*.ts,src/renderer/stores/**/*.ts} : Implement proper inter-process communication (IPC) patterns
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Ensure that adding a new Provider only requires implementing the `coreStream` method according to the standardized interface, without duplicating Agent loop logic.
src/main/presenter/llmProviderPresenter/providers/groqProvider.ts (10)
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Provider files must implement the `coreStream(messages, modelId, temperature, maxTokens)` method, which receives formatted messages and generation parameters, handles tool support (native or via prompt wrapping), makes a single streaming API call, parses provider-specific data, and yields standardized stream events.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Provider files should include provider-specific helper functions such as `formatMessages`, `convertToProviderTools`, `parseFunctionCalls`, and `prepareFunctionCallPrompt` as needed.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Each file in `src/main/presenter/llmProviderPresenter/providers/*.ts` should handle interaction with a specific LLM API, including request/response formatting, tool definition conversion, management of native/non-native tool call mechanisms (prompt wrapping), and standardizing output streams to a common event format.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : Provider implementations must use a standardized interface in their `coreStream` method to `yield` events, decoupling the main Agent loop from provider-specific details.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/providers/*.ts : The `coreStream` method in each Provider must perform a single-pass streaming API request per conversation round and must not contain multi-round tool call loop logic.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/index.ts : The main Agent loop, including the `while` loop for conversation flow, state management, provider interaction, event handling, and frontend communication, must be implemented in `src/main/presenter/llmProviderPresenter/index.ts`.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/index.ts : `src/main/presenter/llmProviderPresenter/index.ts` should manage the overall Agent loop, conversation history, tool execution via `McpPresenter`, and communication with the frontend via `eventBus`.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Maintain separation of concerns by centralizing Agent loop logic in `index.ts` and keeping provider files focused on API interaction and event standardization.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Ensure that adding a new Provider only requires implementing the `coreStream` method according to the standardized interface, without duplicating Agent loop logic.
Learnt from: CR
PR: ThinkInAIXYZ/deepchat#0
File: .cursor/rules/llm-agent-loop.mdc:0-0
Timestamp: 2025-06-30T12:24:03.565Z
Learning: Applies to src/main/presenter/llmProviderPresenter/streamEvents.ts : Define the standardized stream event interface (`LLMCoreStreamEvent`) in a shared location, such as `src/main/presenter/llmProviderPresenter/streamEvents.ts`.
🧬 Code Graph Analysis (1)
src/main/presenter/llmProviderPresenter/index.ts (1)
src/main/presenter/llmProviderPresenter/providers/groqProvider.ts (1)
GroqProvider(25-180)
🪛 Biome (1.9.4)
src/main/presenter/llmProviderPresenter/providers/groqProvider.ts
[error] 26-28: This constructor is unnecessary.
Unsafe fix: Remove the unnecessary constructor.
(lint/complexity/noUselessConstructor)
🔇 Additional comments (7)
src/renderer/src/components/icons/ModelIcon.vue (1)
52-52: LGTM! Groq icon integration follows the established pattern.The implementation correctly adds the Groq icon following the same pattern as other providers.
Also applies to: 67-67, 160-160
src/main/presenter/llmProviderPresenter/index.ts (1)
31-31: LGTM! Clean integration of GroqProvider.The Groq provider is correctly integrated into the factory pattern, following the same instantiation approach as other providers.
Also applies to: 157-158
src/main/presenter/configPresenter/providers.ts (1)
398-412: LGTM! Groq provider configuration is properly structured.The configuration follows the standard provider format with the updated API endpoint including version path.
src/main/presenter/llmProviderPresenter/providers/groqProvider.ts (4)
30-75: LGTM! Method implementations follow the standard pattern.The completions, summaries, and generateText methods correctly delegate to the parent class's openAICompletion method with appropriate message formatting.
5-23: Well-structured interface for Groq model responses.The interface properly maps Groq-specific fields to the expected format with clear documentation of the mapping strategy.
82-152: Excellent implementation of model fetching and configuration management.The method properly handles Groq-specific fields, detects model capabilities, and efficiently updates configurations only when changes are detected. The fallback chain for configuration values is well-structured.
154-179: LGTM! Proper model metadata construction and error handling.The method correctly creates MODEL_META objects with all required fields and includes appropriate error handling with fallback to the parent implementation.
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LGTM |
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Summary by CodeRabbit
New Features
Bug Fixes